Data augmentation.  
We experiment with differentiable augmentation [1]} applied to the sketches for training. We find that mild augmentation performs better in our scenario; in particular, we use translation for the augmentation. While augmentation does not necessarily improve the results when we use 30 input sketches generated from the Photosketch network \({F}\) , we find that it is essential for model training with one or a few hand-drawn sketch inputs. More details are in Section REF .
